{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21377,"status":"ok","timestamp":1690050562286,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"},"user_tz":420},"id":"scKll44AuD1v","outputId":"e14d49d7-ce0b-404b-9c72-3b9379294c66"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting transformers==4.28.0\n","  Downloading transformers-4.28.0-py3-none-any.whl (7.0 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m50.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0) (3.12.2)\n","Collecting huggingface-hub<1.0,>=0.11.0 (from transformers==4.28.0)\n","  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n","\u001b[2K     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xxhash-3.2.0\n","Collecting accelerate\n","  Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.22.4)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.1)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n","Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) 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169MB/s]\n"]}],"source":["!gdown 1a1v06PvGaWmTCWvL33o2C133D90SRzMC # download cw_texts_clean_bert.csv\n","!mkdir output\n","!mkdir /data\n","!mv cw_texts_clean_bert.csv /data"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["2377e41649d34be6b064c2e0b1d8b278","91951540d6a34ba78e41d662ab7dd02e","1f4cf7cf540c4833a3c676fedc94d0d1","606daffb23bb4aa1b5d74a49e77534b7","3a0b6dba9aea48fdae5a03b2bba752d3","787bc2293a2747558af07788743a32c9","033c468774ef439bbb627a397bf4967f","37eb7cc9dce349689a7e16d8b557b67b","abe1dbc5ffb34345a836db82973da36b","b009854ab914445797cc4a6966d3fbc4","f85dc668e5624accaa45b924ad4e17bc","9cbf0b1ca64b4a2da504711d80764706","2579b455601f437691affad7d4b68ee2","23eed055839a42b29846d1f84ba5b76e","c83330b71cf04048a504623d485c333a","5e313113839246779470e6bedc8905d9","f2e3aae350864cb6ae0dd0b727a59183","38f99867cf73401faac1d85f80e6d4ba","5510a820c94743b69c16a38c3464f6d2","04d0e38e696b4000b200a26777e63521","e089b7e3c98d469eb2ad86663ebfcab6","89e80586c3ec46fca0606fe51270ca3f","d3e690f412cb4d3e9f5d924a0bada339","02ec1583a4bc4252bec1ada908b15023","ccd84c15e66b4661a34a6e2147eeebb2","7c41b2c2c853413bbdf1ccffe7c47d9c","f8d292b69c0448ff822f4584d921b4d9","b7aeec241a894630b3bcb4f641bc7de4","641adc461ea34777aea9093ac00d6c9a","9ccb1e601d544aaaa6543cb1bdda3199","58c4006793b04734869dd6fbfe180275","cad1fc9d456d45e9865cf37557b26895","a3143690b2924ead9d62c069506f8ac7","d5ea9b25036b4796a3e0a76cb86e7e9e","fa6dfd2cfc084dd9b4127fb80eaa1ee0","78c04c6fd7614d65af7c2d2664ed7e4e","06d5b2f8268b4996a8755e77d3725a80","61740db11bdc49ca9ba730406bee0433","70a0eed6747a47429c19b05428b368f4","5d017499c6bd4818bfedee8ba1237e73","d63398565793428dac46db5dc58340f8","6573b1b24acd4aa28bd5b7d9716ea460","0f084746fc9b44d0b9c4d8b016ceec5d","308eef5ae6e145f88f8e631e03ecd68c","a3996850bd4f47b59b356c261a240ff5","392b3705811842bd829ff509544c5b97","e1518144742d4a27a908ace220e7e105","15426d5a39544d4eb980543ac59f8445","138e8a53061140d08902d8a14a3145c9","30037fed06b144259c6ae7444ce256ce","4afa4660fec648cabc4a640f66660ace","fb81313569134599b2694cb14a47b657","1498e695f38447eb9c26b84820c56603","95ff8cb1e1e6466c8611c8132da3f5c5","71db6c4f9cfe4330b00102104972b9d2","b813bae29e7c47a68b21eea65f02e010","cc0fcc7bbc4047c4ba7cd01a41a2deb3","64f2d282b7ad45329cd399547a0e55cb","bca340978b8245408cbd7a9c6d0159f1","fda1cb2d216f4af289224de0d506d216","542657bcd3714209857321e091fb4085","ae550b3f230a4b1a9e1774f893dcd77e","e8685ffe615f4dee868d42efb53523e7","0c21e8ae5d1d45239cb8f55af0854946","387d3d1301e24734b791b59b85e9777b","335b0311ebc24bf79f66ea348b0d2069","ac851f8cfc1642efb537335f00782391","c9cc1d929de74bc682f4d016b86d8b72","9f87ed472fb2471eb3db5ea1197042e8","55f253434d40402e91b264b176a80afe","ef321cbd3a4441ae9fda12d6a1a66db0","f039d0839aba46dca37381680d22c553","db4b2a56f51f4572aaf86b54b4cf42a4","ca256644e8234568b62bce340f7fd2c9","589c87b27e814f97b5914e666b0a979c","f3b6626f880547b18b766de6409af689","3b2baa3068a547438c0f38cf0d29def4","33c993f84909456088a87ca649678a43","133ddcf5427348f389cf18843764f29d","bafd9a617d104637b71178a4c8d16813","614f151ce2b144ca9930d7c054740bdb","7370b379ac854946b2756d6bfa187e3a","a700ca9b62ef4924bfd54082d588d41f","847933d22ae6471d9f8a0d9be994516e","e40becf0293d4591b23424b444173180","cce91878a916459cad78592be945a952","f21b35c55c8246df9b9ee924d2f13d9e","b74b86511dd747a6b52a64bf691c1a3f","e13096e69ea647988709574e6a6c28f3","1616c3246ced489cb049596134af26bb","568c5a99874140fb89cf1e02dca7a7fd","d29c4c5dbb0644c6ac746040d897611d","7571bcfd32be4c4bb4272ea804648e09","0e2caeeffdd64f34ac5208fd9d53b6db","2767bc8a0d2041f0b7a6406d99e3e550","7fbbb6bf52e54ab88509d1812d4a1311","dfc64430dd154407b50b2d5d1f4d33aa","d827fbe36f594525a42ec6f660fd40e3","096ab29243854b17b1a6ca0384de6b1b","672a4c97100c4786965eec9416914df4","eb49b45cab574e05a406d549781923ea","c62fe5026ef540b9a1a5687e58ef5bf2","b805aab10f1d43fcaf7de04fa3655be3","5b4673e02ded4c1f9d4196ec15ff96de","e6c39d83f55643b796310f5c6e34f7c4","9c4155259f27440e97bbb7d35f49f109","7535a9ee58174938b0de89b2f7197ae6","8baea6181d634a0b969ea558b7da8bea","41988e1562ad4ed58bb5bbd90c5e2f36","fe3a9fc11033455e8b2e09e3b215fa09"]},"id":"_UUtlbRAySQ_","outputId":"25f5cd35-e244-4dbd-f624-e15ede206921","executionInfo":{"status":"ok","timestamp":1690056431787,"user_tz":420,"elapsed":5866639,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-3-f0dfa708a4ad>:26: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'] = ''\n","<ipython-input-3-f0dfa708a4ad>:27: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['year'] = train['year'].astype(int)\n","<ipython-input-3-f0dfa708a4ad>:28: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['month'] = train['month'].astype(int)\n"]},{"output_type":"stream","name":"stdout","text":["23868\n"]},{"output_type":"stream","name":"stderr","text":["  0%|          | 0/22572 [00:00<?, ?it/s]<ipython-input-3-f0dfa708a4ad>:34: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 0\n","<ipython-input-3-f0dfa708a4ad>:32: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 1\n","100%|██████████| 22572/22572 [00:06<00:00, 3260.68it/s]\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/270 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2377e41649d34be6b064c2e0b1d8b278"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9cbf0b1ca64b4a2da504711d80764706"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/224k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d3e690f412cb4d3e9f5d924a0bada339"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/454k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d5ea9b25036b4796a3e0a76cb86e7e9e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a3996850bd4f47b59b356c261a240ff5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/21924 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b813bae29e7c47a68b21eea65f02e010"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/648 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ac851f8cfc1642efb537335f00782391"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1296 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"33c993f84909456088a87ca649678a43"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/437M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e13096e69ea647988709574e6a6c28f3"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['classifier.weight', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","Some weights of the model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['classifier.weight', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='13710' max='13710' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [13710/13710 1:29:51, Epoch 10/10]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Mse</th>\n","      <th>Mae</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>500</td>\n","      <td>1.350500</td>\n","      <td>1.084972</td>\n","      <td>1.084972</td>\n","      <td>0.602693</td>\n","      <td>0.723009</td>\n","      <td>0.662037</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>0.880700</td>\n","      <td>1.051073</td>\n","      <td>1.051073</td>\n","      <td>0.570722</td>\n","      <td>0.731663</td>\n","      <td>0.697531</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>0.809800</td>\n","      <td>1.067349</td>\n","      <td>1.067349</td>\n","      <td>0.551974</td>\n","      <td>0.727508</td>\n","      <td>0.700617</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>0.624500</td>\n","      <td>1.107405</td>\n","      <td>1.107405</td>\n","      <td>0.591714</td>\n","      <td>0.717282</td>\n","      <td>0.688272</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>0.637300</td>\n","      <td>0.859338</td>\n","      <td>0.859337</td>\n","      <td>0.517090</td>\n","      <td>0.780613</td>\n","      <td>0.706790</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>0.609800</td>\n","      <td>0.931702</td>\n","      <td>0.931702</td>\n","      <td>0.542799</td>\n","      <td>0.762138</td>\n","      <td>0.712963</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>0.554100</td>\n","      <td>0.833388</td>\n","      <td>0.833388</td>\n","      <td>0.481238</td>\n","      <td>0.787238</td>\n","      <td>0.745370</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>0.496700</td>\n","      <td>0.846705</td>\n","      <td>0.846705</td>\n","      <td>0.460094</td>\n","      <td>0.783838</td>\n","      <td>0.729938</td>\n","    </tr>\n","    <tr>\n","      <td>4500</td>\n","      <td>0.416400</td>\n","      <td>0.834755</td>\n","      <td>0.834755</td>\n","      <td>0.581662</td>\n","      <td>0.786889</td>\n","      <td>0.705247</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>0.450200</td>\n","      <td>0.847546</td>\n","      <td>0.847545</td>\n","      <td>0.477798</td>\n","      <td>0.783623</td>\n","      <td>0.719136</td>\n","    </tr>\n","    <tr>\n","      <td>5500</td>\n","      <td>0.454600</td>\n","      <td>0.873767</td>\n","      <td>0.873767</td>\n","      <td>0.466796</td>\n","      <td>0.776929</td>\n","      <td>0.745370</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>0.375300</td>\n","      <td>0.882961</td>\n","      <td>0.882961</td>\n","      <td>0.451003</td>\n","      <td>0.774582</td>\n","      <td>0.750000</td>\n","    </tr>\n","    <tr>\n","      <td>6500</td>\n","      <td>0.374000</td>\n","      <td>0.803201</td>\n","      <td>0.803201</td>\n","      <td>0.440383</td>\n","      <td>0.794944</td>\n","      <td>0.739198</td>\n","    </tr>\n","    <tr>\n","      <td>7000</td>\n","      <td>0.366200</td>\n","      <td>1.000226</td>\n","      <td>1.000226</td>\n","      <td>0.534657</td>\n","      <td>0.744644</td>\n","      <td>0.699074</td>\n","    </tr>\n","    <tr>\n","      <td>7500</td>\n","      <td>0.332700</td>\n","      <td>0.863325</td>\n","      <td>0.863325</td>\n","      <td>0.479362</td>\n","      <td>0.779595</td>\n","      <td>0.750000</td>\n","    </tr>\n","    <tr>\n","      <td>8000</td>\n","      <td>0.317600</td>\n","      <td>0.820603</td>\n","      <td>0.820603</td>\n","      <td>0.436919</td>\n","      <td>0.790502</td>\n","      <td>0.754630</td>\n","    </tr>\n","    <tr>\n","      <td>8500</td>\n","      <td>0.323200</td>\n","      <td>0.843360</td>\n","      <td>0.843360</td>\n","      <td>0.447842</td>\n","      <td>0.784692</td>\n","      <td>0.762346</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>0.307900</td>\n","      <td>0.826583</td>\n","      <td>0.826583</td>\n","      <td>0.464924</td>\n","      <td>0.788975</td>\n","      <td>0.726852</td>\n","    </tr>\n","    <tr>\n","      <td>9500</td>\n","      <td>0.296600</td>\n","      <td>0.895549</td>\n","      <td>0.895549</td>\n","      <td>0.499186</td>\n","      <td>0.771368</td>\n","      <td>0.742284</td>\n","    </tr>\n","    <tr>\n","      <td>10000</td>\n","      <td>0.293900</td>\n","      <td>0.843243</td>\n","      <td>0.843243</td>\n","      <td>0.446880</td>\n","      <td>0.784722</td>\n","      <td>0.757716</td>\n","    </tr>\n","    <tr>\n","      <td>10500</td>\n","      <td>0.269300</td>\n","      <td>0.809539</td>\n","      <td>0.809539</td>\n","      <td>0.440073</td>\n","      <td>0.793326</td>\n","      <td>0.765432</td>\n","    </tr>\n","    <tr>\n","      <td>11000</td>\n","      <td>0.289000</td>\n","      <td>0.840665</td>\n","      <td>0.840665</td>\n","      <td>0.458544</td>\n","      <td>0.785380</td>\n","      <td>0.762346</td>\n","    </tr>\n","    <tr>\n","      <td>11500</td>\n","      <td>0.269200</td>\n","      <td>0.841869</td>\n","      <td>0.841869</td>\n","      <td>0.450398</td>\n","      <td>0.785073</td>\n","      <td>0.759259</td>\n","    </tr>\n","    <tr>\n","      <td>12000</td>\n","      <td>0.264400</td>\n","      <td>0.799720</td>\n","      <td>0.799720</td>\n","      <td>0.435005</td>\n","      <td>0.795833</td>\n","      <td>0.763889</td>\n","    </tr>\n","    <tr>\n","      <td>12500</td>\n","      <td>0.260000</td>\n","      <td>0.824847</td>\n","      <td>0.824847</td>\n","      <td>0.436415</td>\n","      <td>0.789418</td>\n","      <td>0.756173</td>\n","    </tr>\n","    <tr>\n","      <td>13000</td>\n","      <td>0.236700</td>\n","      <td>0.834635</td>\n","      <td>0.834635</td>\n","      <td>0.442949</td>\n","      <td>0.786919</td>\n","      <td>0.754630</td>\n","    </tr>\n","    <tr>\n","      <td>13500</td>\n","      <td>0.270900</td>\n","      <td>0.830920</td>\n","      <td>0.830920</td>\n","      <td>0.438188</td>\n","      <td>0.787868</td>\n","      <td>0.751543</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["{'loss': 1.3505, 'learning_rate': 1.927060539752006e-05, 'epoch': 0.36, 'step': 500}\n","{'eval_loss': 1.0849722623825073, 'eval_mse': 1.0849722623825073, 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0.7878677536892418, 'eval_accuracy': 0.7515432098765432, 'eval_runtime': 5.2169, 'eval_samples_per_second': 124.211, 'eval_steps_per_second': 7.859, 'epoch': 9.85, 'step': 13500}\n","{'train_runtime': 5394.9846, 'train_samples_per_second': 40.638, 'train_steps_per_second': 2.541, 'total_flos': 5.768394985156608e+16, 'train_loss': 0.44670590763105955, 'epoch': 10.0, 'step': 13710}\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[[7.7449875 ]\n"," [7.4523125 ]\n"," [7.507855  ]\n"," [7.562033  ]\n"," [7.604245  ]\n"," [8.005036  ]\n"," [7.762905  ]\n"," [8.079368  ]\n"," [7.730903  ]\n"," [7.411073  ]\n"," [7.4102387 ]\n"," [7.0169263 ]\n"," [0.04989319]\n"," [0.74488044]\n"," [0.04989319]\n"," [0.04989319]\n"," [0.04989319]\n"," [0.04989319]\n"," [0.04989319]\n"," [0.04989319]]\n","0.8698185766994447\n","0.8014049598942927\n","Now whole corpus\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/23868 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"672a4c97100c4786965eec9416914df4"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Now predicting\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[[5.2749763]\n"," [4.291986 ]\n"," [5.1619515]\n"," [3.343033 ]\n"," [3.7990487]\n"," [4.427507 ]\n"," [2.5283716]\n"," [3.7022865]\n"," [3.6537125]\n"," [4.1721854]\n"," [4.2905617]\n"," [3.8646462]\n"," [4.9252057]\n"," [5.2852693]\n"," [4.1785717]\n"," [3.5903099]\n"," [3.685331 ]\n"," [3.8504355]\n"," [3.0339248]\n"," [3.8545408]]\n","DF saved\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1492' max='1492' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1492/1492 03:11]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'eval_loss': 0.3065298795700073,\n"," 'eval_mse': 0.3065298795700073,\n"," 'eval_mae': 0.22832010686397552,\n"," 'eval_r2': 0.9127988932734308,\n"," 'eval_accuracy': 0.89295290765879,\n"," 'eval_runtime': 191.6104,\n"," 'eval_samples_per_second': 124.565,\n"," 'eval_steps_per_second': 7.787,\n"," 'epoch': 10.0}"]},"metadata":{},"execution_count":3}],"source":["# the following lines (177 in total) are directly copied from Haffner et al. (2023), Political Analysis\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from datasets import Dataset\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","from sklearn.metrics import mean_absolute_error\n","from transformers import TrainingArguments, Trainer\n","import torch\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import mean_squared_error\n","from sklearn.metrics import r2_score\n","from numpy.random import seed\n","from tqdm import tqdm\n","seed(1)\n","\n","final = pd.read_csv('/data/cw_texts_clean_bert.csv')\n","\n","print(len(final))\n","final['fatalities_log'] = final['fatalities_log'].astype(float)\n","\n","train = final[final['year'] < 2021]\n","train.reset_index(inplace=True, drop=True)\n","test = final[final['year'] == 2021]\n","test.reset_index(inplace=True, drop=True)\n","\n","train['split_dummy'] = ''\n","train['year'] = train['year'].astype(int)\n","train['month'] = train['month'].astype(int)\n","\n","for i in tqdm(range(len(train))):\n","    if train['year'][i] == 2020 and train['month'][i] > 6:\n","        train['split_dummy'][i] = 1\n","    else:\n","        train['split_dummy'][i] = 0\n","\n","train['split_dummy'].value_counts()\n","\n","train1 = train[train['split_dummy'] == 0]\n","vali = train[train['split_dummy'] == 1]\n","\n","X_train = train1['final_text']\n","y_train = train1['fatalities_log']\n","X_vali = vali['final_text']\n","y_vali = vali['fatalities_log']\n","X_test = test['final_text']\n","y_test = test['fatalities_log']\n","X_train = pd.DataFrame(X_train)\n","X_test = pd.DataFrame(X_test)\n","X_vali = pd.DataFrame(X_vali)\n","y_vali = pd.DataFrame(y_vali)\n","y_train = pd.DataFrame(y_train)\n","y_test = pd.DataFrame(y_test)\n","\n","df_train = pd.concat([X_train, y_train], axis=1)\n","df_val = pd.concat([X_vali, y_vali], axis=1)\n","df_test = pd.concat([X_test, y_test], axis=1)\n","\n","\n","raw_train_ds = Dataset.from_pandas(df_train)\n","raw_val_ds = Dataset.from_pandas(df_val)\n","raw_test_ds = Dataset.from_pandas(df_test)\n","\n","BASE_MODEL = \"snowood1/ConfliBERT-scr-uncased\"\n","LEARNING_RATE = 2e-5 # decreased from 2e-3\n","MAX_LENGTH = 512 # increased from 256\n","BATCH_SIZE = 16\n","EPOCHS = 10 # reduced from 20\n","\n","tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)\n","def model_init():\n","    model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL, num_labels=1, ignore_mismatched_sizes=True)\n","    # for param in model.bert.parameters():\n","    #     param.requires_grad = False\n","    return model\n","\n","\n","ds = {\"train\": raw_train_ds, \"validation\": raw_val_ds, \"test\": raw_test_ds}\n","\n","\n","def preprocess_function(examples):\n","    label = examples[\"fatalities_log\"]\n","    examples = tokenizer(examples[\"final_text\"], truncation=True, padding=\"max_length\", max_length=MAX_LENGTH)\n","\n","    # Change this to real number\n","    examples[\"label\"] = float(label)\n","    return examples\n","\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","\n","def compute_metrics_for_regression(eval_pred):\n","    logits, labels = eval_pred\n","    labels = labels.reshape(-1, 1)\n","\n","    mse = mean_squared_error(labels, logits)\n","    mae = mean_absolute_error(labels, logits)\n","    r2 = r2_score(labels, logits)\n","    single_squared_errors = ((logits - labels).flatten() ** 2).tolist()\n","\n","    # Compute accuracy\n","    # Based on the fact that the rounded score = true score only if |single_squared_errors| < 0.5\n","    accuracy = sum([1 for e in single_squared_errors if e < 0.25]) / len(single_squared_errors)\n","\n","    return {\"mse\": mse, \"mae\": mae, \"r2\": r2, \"accuracy\": accuracy}\n","\n","training_args = TrainingArguments(\n","    output_dir=\"/data/output/conflibert\",\n","    learning_rate=LEARNING_RATE,\n","    per_device_train_batch_size=BATCH_SIZE,\n","    per_device_eval_batch_size=BATCH_SIZE,\n","    num_train_epochs=EPOCHS,\n","    evaluation_strategy=\"steps\",\n","    eval_steps=500,\n","    save_total_limit=10,\n","    load_best_model_at_end=True,\n","    weight_decay=0.01,\n","    seed=123\n",")\n","\n","class RegressionTrainer(Trainer):\n","    def compute_loss(self, model, inputs, return_outputs=False):\n","        labels = inputs.pop(\"labels\")\n","        outputs = model(**inputs)\n","        logits = outputs[0][:, 0]\n","        loss = torch.nn.functional.mse_loss(logits, labels)\n","        return (loss, outputs) if return_outputs else loss\n","\n","\n","trainer = RegressionTrainer(\n","    model_init=model_init,\n","    args=training_args,\n","    train_dataset=ds[\"train\"],\n","    eval_dataset=ds[\"validation\"],\n","    compute_metrics=compute_metrics_for_regression,\n",")\n","\n","trainer.train()\n","\n","for obj in trainer.state.log_history:\n","    print(obj)\n","\n","trainer.eval_dataset=ds[\"test\"]\n","trainer.evaluate()\n","\n","raw_pred_test, _, _ = trainer.predict(ds[\"test\"])\n","print(raw_pred_test[:20])\n","pd.DataFrame(raw_pred_test).to_csv('/data/cw_pred_test_zero.csv')\n","\n","df_results = pd.DataFrame(columns=['Test_MSE','Test_R2'], index=range(0,1))\n","mse = mean_squared_error(ds['test']['fatalities_log'], raw_pred_test)\n","r_squared = r2_score(ds['test']['fatalities_log'], raw_pred_test)\n","print(mse)\n","print(r_squared)\n","df_results['Test_MSE'][0] = mse\n","df_results['Test_R2'][0] = r_squared\n","df_results.to_csv('/data/bert_metrics.csv', index=False)\n","\n","## Whole CW\n","print('Now whole corpus')\n","raw_train1_ds = Dataset.from_pandas(final)\n","ds = {\"test\": raw_train1_ds}\n","\n","## Create dataset\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","print('Now predicting')\n","raw_pred, _, _ = trainer.predict(ds[\"test\"])\n","print(raw_pred[:20])\n","pd.DataFrame(raw_pred).to_csv('/data/cw_bert_pred_zero.csv')\n","\n","print('DF saved')\n","\n","trainer.eval_dataset=ds[\"test\"]\n","trainer.evaluate()"]}],"metadata":{"accelerator":"GPU","colab":{"machine_shape":"hm","provenance":[{"file_id":"1gRvnvY9Gf3Y4MkpXcMTH9ZrZ0hwYFGPN","timestamp":1690050282383},{"file_id":"1yZcAvzPRSAOGBxYDVgtDywAJgZmGD8AG","timestamp":1689538072188},{"file_id":"1wyzuYfVsl5mPnzGzJGqKODItBOK8Nj46","timestamp":1689536289631}],"gpuType":"A100","authorship_tag":"ABX9TyNvnag7vwlIsQjTt8Gf8+CY"},"kernelspec":{"display_name":"Python 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